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<font color=red size=5> ''THIS PAGE IS UNDER CONSTRUCTION'' </font><br>
 
 
__TOC__
 
__TOC__
=Running Matlab interactively=
+
= Running Matlab interactively =
Matlab is accessible to all HPRC users within the terms of our license agreement. If you have particular concerns about whether specific usage falls within the TAMU HPRC license, please send an email to HPRC Helpdesk.
+
Matlab is accessible to all HPRC users within the terms of our license agreement. If you have particular concerns about whether specific usage falls within the TAMU HPRC license, please send an email to HPRC Helpdesk. You can start a Matlab session either directly on a login node or through our portal
  
==Setting up the environment==
+
== Running Matlab on a login node ==
To be able to use matlab, the Matlab module needs to be loaded first. This can be done using the following command:
 
[ netID@cluster ~]$ '''module load Matlab/R2016b'''
 
  
This will setup the environment for Matlab version R2016b. To see a list of all installed versions, use the following command:
+
To be able to use Matlab, the Matlab module needs to be loaded first. This can be done using the following command:
 +
[ netID@cluster ~]$ '''module load Matlab/R2019a'''
 +
 
 +
This will setup the environment for Matlab version R2019a. To see a list of all installed versions, use the following command:
 
  [ netID@cluster ~]$ '''module spider Matlab'''
 
  [ netID@cluster ~]$ '''module spider Matlab'''
 
<font color=teal>'''Note:''' New versions of software become available periodically. Version numbers may change.</font>
 
<font color=teal>'''Note:''' New versions of software become available periodically. Version numbers may change.</font>
  
==Starting Matlab==
 
 
To start matlab, use the following command:  
 
To start matlab, use the following command:  
 
  [ netID@cluster ~]$ '''matlab'''
 
  [ netID@cluster ~]$ '''matlab'''
  
Depending on your X server settings, this will start either the Matlab GUI or the Matlab command line interface. To force matlab to start in command line interface mode, use the following command with the appropriate flags:
+
Depending on your X server settings, this will start either the Matlab GUI or the Matlab command-line interface. To start Matlab in command-line interface mode, use the following command with the appropriate flags:
 
  [ netID@cluster ~]$ '''matlab -nosplash -nodisplay'''
 
  [ netID@cluster ~]$ '''matlab -nosplash -nodisplay'''
  
By default, Matlab can execute a large number of built-in operators and function multi-threaded and will use as many threads (i.e. cores). Since login nodes are shared among all users, HPRC restricted the number of computational threads to 4. This should suffice for most cases. To explicitly disable multi-threading, the following command with the appropriate flag can be used:
+
By default, Matlab will execute a large number of built-in operators and functions multi-threaded and will use as many threads (i.e. cores) as are available on the node. Since login nodes are shared among all users, HPRC restricts the number of computational threads to 8. This should suffice for most cases. Speedup achieved through multi-threading depends on many factors and in certain cases. To explicitly change the number of computational threads, use the following Matlab command:
 +
>>feature('NumThreads',4);
 +
 
 +
This will set the number of computational threads to 4.
 +
 
 +
To completely disable multi-threading, use the -singleCompThread option when starting Matlab:
 
  [ netID@cluster ~]$ '''matlab -singleCompThread'''
 
  [ netID@cluster ~]$ '''matlab -singleCompThread'''
  
 
{{:SW:Login_Node_Warning}}
 
{{:SW:Login_Node_Warning}}
  
==matlabsubmit: running matlab codes on the compute nodes==
+
== Running Matlab through the hprc portal ==
 +
 
 +
HPRC provides a portal through which users can start an interactive Matlab GUI session inside a web browser. For more information how to use the portal see our [[SW:Portal | HPRC OnDemand Portal]] section
  
TAMU HPRC provides a tool named '''matlabsubmit''' to automate the process of running Matlab simulations on the compute nodes without the need to create your own batch script. This is the recommended way of running Matlab simulations on the compute nodes since it guarantees all batch resources are set correctly. In addition, matlabsubmit will also set the number of Matlab computational threads. If any additional Matlab workers are requested, it will automatically create a Matlab ''parpool'' using the correct profile (using a ''local'' profile for single node and a ''ClusterProfile'' for multiple nodes). 
+
= Running Matlab through the batch system =
  
 +
 +
HPRC developed a tool named '''matlabsubmit''' to run Matlab simulations on the HPRC compute nodes without the need to create your own batch script and without the need to start a Matlab session.  '''matlabsubmit''' will automatically generate a batch script with the correct requirements. In addition, '''matlabsubmit''' will also generate boilerplate Matlab code to set up the environment (e.g. the number of computational threads) and, if needed, will start a ''parpool'' using the correct Cluster Profile (''local'' if all workers fit on a single node and a cluster profile when workers are distribued over multiple nodes)
  
 
To submit your Matlab script, use the following command:
 
To submit your Matlab script, use the following command:
 
<pre>
 
<pre>
  matlabsubmit myscript.m
+
[ netID@cluster ~]$ matlabsubmit myscript.m
 
</pre>
 
</pre>
  
When executing, matlabsubmit will do the following:
+
In the above example, '''matlabsubmit''' will use all default values for runtime, memory requirements, the number of workers, etc. To specify resources, you can use the command-line options of '''matlabsubmmit'''. For example:
* generate boiler plate Matlab code to setup the matlab environment (e.g. #threads, #workers) <br>
 
* generate a batch script with all resources set correctly and the command to run matlab <br>
 
* submit the generated batch script to the batch scheduler and return control back to the user <br>
 
 
 
In Addition, matlabsubmit will also save the complete workspace after the matlab script finishes executing.
 
 
 
 
=== Example 1: basic use ===
 
 
 
The following example shows the simplest use of matlabsubmit. It will execute matlab script ''test.m'' using default values for batch resources and Matlab resources. matlabsubmit will also print some useful information to the screen. As can be seen in the example, it will show the Matlab resources requested (e.g. #threads, #workers), the submit command that will be used to submit the job, the batch scheduler JobID, and the location of output generated by Matlab and the batch scheduler.
 
  
 
<pre>
 
<pre>
-bash-4.1$ matlabsubmit test.m
+
[ netID@cluster ~]$ matlabsubmit -t 07:00 -s 4 myscript.m
 
 
===============================================
 
Running Matlab script with following parameters
 
-----------------------------------------------
 
Script    : test.m
 
Workers    : 0
 
Nodes      : 1
 
Mem/proc  : 2500
 
#threads  : 8
 
===============================================
 
 
 
bsub  -e MatlabSubmitLOG1/lsf.err -o MatlabSubmitLOG1/lsf.out 
 
      -L /bin/bash -n 8 -R span[ptile=8] -W 02:00 -M 2500
 
      -R rusage[mem=2500]     
 
      -J test1 MatlabSubmitLOG1/submission_script
 
 
 
Verifying job submission parameters...
 
Verifying project account...
 
    Account to charge:  082839397478
 
        Balance (SUs):    81535.6542
 
        SUs to charge:        16.0000
 
Job <2847580> is submitted to default queue <sn_regular>.
 
 
 
-----------------------------------------------
 
matlabsubmit ID        : 1
 
matlab output file    : MatlabSubmitLOG1/matlab.log
 
LSF/matlab output file : MatlabSubmitLOG1/lsf.out
 
LSF/matlab error file  : MatlabSubmitLOG1/lsf.err
 
-bash-4.1$
 
 
</pre>
 
</pre>
  
 +
will set the wall-time to 7 hours and makes sure Matlab will use 4 computational threads for its run ( '''matlabsubmit''' will also request 4 cores).
  
The matlab script ''test.m'' has to be in the current directory. Control will be returned immediately after executing the matlabsubmit command. To check the run status or kill a job, use the respective batch scheduler commands (e.g. '''bjobs''' and '''bkill''' on ada). matlabsubmit will create a sub directory named '''MatlabSubmitLOG<N>''' (where '''N''' is the matlabsubmit ID). In this directory matlabsubmit will store all its relevant files; the generated batch script, matlab driver, redirected output and error, and a copy of the workspace (after the job is done). A listing of this directory will show the following files:
+
To see all options for '''matlabsubmit''' use the '''-h''' flag
 
 
* '''lsf.err''' redirected error
 
* '''lsf.out''' redirected output (both LSF and Matlab)
 
* '''matlab.log''' redirected Matlab screen output
 
* '''matlabsubmit_wrapper.m''' Matlab code that sets #threads and calls user function
 
* '''submission_script''' the generated LSF batch script
 
* '''workspace.mat''' a copy of the matlab workspace (after execution has finished)
 
 
 
===Options with matlabsubmit===
 
 
 
The example above showed the most simple case of using matlabsubmit. No options where specified and matlabsubmit used default values for requested resources. However, matlabsubmit provides a number of options to set batch resources (e.g. walltime, memory) as well as matlab related options (e.g. number of threads to use, number of workers, etc). To see all the available options you can use the "'''-h'''" option.  See below for the output of "'''matlabsubmit -h'''":
 
  
 
<pre>
 
<pre>
 +
[ netID@cluster ~]$ matlabsubmit -h
 +
Usage: /sw/hprc/sw/Matlab/bin/matlabsubmit [options] SCRIPTNAME
  
-bash-4.1$ matlabsubmit -h
+
This tool automates the process of running Matlab codes on the compute nodes.
/software/hprc/Matlab/bin/matlabsubmit: option requires an argument -- h
 
Usage: /software/hprc/Matlab/bin/matlabsubmit [options] SCRIPTNAME
 
 
 
This tools automates the process of running matlab codes on the compute nodes.
 
  
 
OPTIONS:
 
OPTIONS:
Line 115: Line 72:
 
    
 
    
 
DEFAULT VALUES:
 
DEFAULT VALUES:
   memory  : 2500 per core  
+
   memory  : 2000 per core  
 
   time    : 02:00
 
   time    : 02:00
 
   workers  : 0
 
   workers  : 0
Line 121: Line 78:
 
   threading: on, 8 threads
 
   threading: on, 8 threads
  
 
-bash-4.1$
 
 
</pre>
 
</pre>
  
For example, the command matlabsubmit -t "03:27" -m 17000 -s 20 myscript.m will request 17gb of memory and 3 hours and 27 minutes of computing time. It will also set the number of computational threads in Matlab to 20 and execute the Matlab script myscript.m.
 
<br>
 
<br>
 
  
 
'''NOTE''' when using the '''-f''' flag to execute a function instead of a script, the function call must be enclosed with double quotes when it contains parentheses. For example: '''matlabsubmit -f "myfunc(21)"'''
 
'''NOTE''' when using the '''-f''' flag to execute a function instead of a script, the function call must be enclosed with double quotes when it contains parentheses. For example: '''matlabsubmit -f "myfunc(21)"'''
  
=== Example 2: Utilizing Matlab workers (single node)  ===
+
<br>
To utilize additional workers used by Matlab's parallel features such as ''parfor'',''spmd'', and ''distributed'' matlabsubmit provides the option to specify the number of workers. This is done using the ''-w <N>'' flag (where <N> represents the number of workers). The following example shows a simple case of using additional workers; in this case 8 workers
+
 
<pre>
+
When executing, '''matlabsubmit''' will do the following:
 +
* generate boilerplate Matlab code to setup the Matlab environment (e.g. #threads, #workers) <br>
 +
* generate a batch script with all resources set correctly and the command to run Matlab <br>
 +
* submit the generated batch script to the batch scheduler and return control back to the user <br>
  
-bash-4.1$ matlabsubmit -w 8 test.m
 
===============================================
 
Running Matlab script with following parameters
 
-----------------------------------------------
 
Script    : test.m
 
Workers    : 8
 
Nodes      : 1
 
Mem/proc  : 2500
 
#threads  : 1
 
===============================================
 
  
bsub  -e MatlabSubmitLOG5/lsf.err -o MatlabSubmitLOG5/lsf.out 
+
For detailed examples on using matlabsubmit see the [[ SW:Matlab_matlabsubmit | examples ]] section.
      -L /bin/bash -n 9 -R span[ptile=9] -W 02:00 -M 2500
 
      -R rusage[mem=2500]  
 
      -J test5 MatlabSubmitLOG5/submission_script
 
  
Verifying job submission parameters...
 
Verifying project account...
 
    Account to charge:  082839397478
 
        Balance (SUs):    80533.2098
 
        SUs to charge:        18.0000
 
Job <2901543> is submitted to default queue <sn_regular>.
 
  
-----------------------------------------------
 
matlabsubmit ID        : 5
 
matlab output file    : MatlabSubmitLOG5/matlab.log
 
LSF/matlab output file : MatlabSubmitLOG5/lsf.out
 
LSF/matlab error file  : MatlabSubmitLOG5/lsf.err
 
  
-bash-4.1$
+
= Using Matlab Parallel Toolbox on HPRC Resources=
  
</pre>
 
  
In this example, matlabsubmit will first execute matlab code to create a ''parpool'' with 8 workers (using the local profile). As can be seen in the output, in this case, matlabsubmit requests 9 cores: 1 core for the client and 8 cores for the workers. The only exception is when the user requests 20 workers. In that case, matlabsubmit will request 20 cores.
+
<font color=red> ''THIS SECTION IS UNDER CONSTRUCTION'' </font><br>
  
 +
In this section, we will focus on utilizing the Parallel toolbox on HPRC cluster. For a general intro to the Parallel Toolbox see the  [https://www.mathworks.com/help/parallel-computing/index.html?s_tid=CRUX_lftnav  parallel toolbox ] section on the Mathworks website. Here we will discuss how to use Matlab Cluster profiles to distribute workers over multiple nodes.
  
=== Example 3: Utilizing Matlab workers (multi node)  ===
+
The central concept in most of the discussion below is the '''TAMUClusterProperties''' object which we will discuss in more detail in the next section
matlabsubmit provides excellent options for Matlab runs that need more than 20 workers (maximum for single node) and/or when the Matlab workers need to be distributed among multiple nodes. Reasons for distributing workers among different nodes include: need to use certain resources such as gpu on multiple nodes, enable multi threading on every worker, and use the available memory on multiple nodes.
 
The following example shows how to run a matlab simulation that utilizes 24 workers, where every node will run 4 workers (i.e. the workers will be distributed among 24/4 = 6 nodes).
 
<pre>
 
-bash-4.1$ matlabsubmit -w 24 -p 4 test.m
 
===============================================
 
Running Matlab script with following parameters
 
-----------------------------------------------
 
Script    : test.m
 
Workers    : 24
 
Nodes      : 6
 
Mem/proc  : 2500
 
#threads  : 1
 
===============================================
 
  
... starting matlab batch. This might take some time.
 
See MatlabSubmitLOG8/matlab-batch-commands.log
 
...Starting Matlab from host: login4
 
MATLAB is selecting SOFTWARE OPENGL rendering.
 
  
                                          < M A T L A B (R) >
+
== Cluster Profiles ==
                                Copyright 1984-2016 The MathWorks, Inc.
+
Matlab Cluster Profiles provide an interface to define properties of how and where to start Matlab workers. There are two kinds of profiles.
                                R2016a (9.0.0.341360) 64-bit (glnxa64)
 
                                            February 11, 2016
 
  
 +
* local profiles: parallel processing is limited to the same node the Matlab client is running.
 +
* cluster profiles: parallel processing can span multiple nodes; profile interacts with a batch scheduler (e.g. SLURM on terra).
 
   
 
   
To get started, type one of these: helpwin, helpdesk, or demo.
+
'''NOTE:''' we will not discuss ''local profiles'' any further here. Processing using a local profile is exactly the same as processing using cluster profiles.
For product information, visit www.mathworks.com.
 
 
... Interactive Matlab session, multi threading reduced to 4
 
  
Academic License
 
  
 +
=== Importing Cluster Profile ===
  
commandToRun =
+
For your convenience, HPRC already created a custom Cluster Profile. Using the profile, you can define how many workers you want, how you want to distribute the workers over the nodes, how many computational threads to use, how long to run, etc. Before you can use this profile you need to import it first. This can be done using by calling the following Matlab function.
 
 
bsub -L /bin/bash -J Job1 -o '/general/home/pennings/Job1/Job1.log' -n 25 -M 2500
 
    -R rusage[mem=2500] -R "span[ptile=4]" -W 02:00     
 
    "source /general/home/pennings/Job1/mdce_envvars ;
 
    /general/software/x86_64/tamusc/Matlab/toolbox/tamu/profiles/lsfgeneric/communicatingJobWrapper.sh"
 
 
 
 
 
job =
 
 
 
Job
 
 
 
    Properties:
 
 
 
                  ID: 1
 
                Type: pool
 
            Username: pennings
 
                State: running
 
          SubmitTime: Mon Aug 01 12:15:15 CDT 2016
 
            StartTime:
 
    Running Duration: 0 days 0h 0m 0s
 
      NumWorkersRange: [25 25]
 
 
 
      AutoAttachFiles: true
 
  Auto Attached Files: /general/home/pennings/MatlabSubmitLOG8/matlabsubmit_wrapper.m
 
                      /general/home/pennings/test.m
 
        AttachedFiles: {}
 
      AdditionalPaths: {}
 
 
 
    Associated Tasks:
 
 
 
      Number Pending: 25
 
      Number Running: 0
 
      Number Finished: 0
 
    Task ID of Errors: []
 
  Task ID of Warnings: []
 
 
 
 
 
 
 
 
 
-----------------------------------------------
 
matlabsubmit JOBID            : 8
 
batch  output file (client)  : Job1/Task1.diary.txt
 
batch  output files (workers) : Job1/Task[2-25].diary.txt
 
Done
 
 
 
-bash-4.1$
 
  
 +
<pre>
 +
>>tamuprofile.importProfile()
 
</pre>
 
</pre>
  
As can be seen the output is very different from the previous examples. When a job uses multiple nodes the approach matlabsubmit uses is a bit different. matlabsubmit will start a regular ''interactive'' matlab session and from within it will run the Matlab ''batch'' command using the '''TAMUG''' cluster profile. It will then exit Matlab while the Matlab script is executed on the compute nodes.  <br>
 
<br>
 
The contents of the MatlabSubmitLOG directory are also slightly different. A listing will show the following files:
 
 
* '''matlab-batch-commands.log''' screen output from Matlab 
 
* '''matlabsubmit_driver.m'''  Matlab code that sets up the cluster profile and calls Matlab ''batch''
 
* '''matlabsubmit_wrapper.m''' Matlab code that sets #threads and calls user function
 
* '''submission_script''' The actual command to start Matlab
 
 
In addition to the MatlabSubmitLOG directory created by matlabsubmit, Matlab will also create a directory named '''Job<N>''' used by the cluster profile to store meta data, log files, and screen output. The '''*.diary.txt''' text files will show screen output for the client and all the workers.
 
 
=== Hybrid jobs (utilize workers and enable multi threading) ===
 
 
By default matlabsubmit will turn off the multi threading features when workers are requested. To override this, use both the '''-w''' flag and the '''-s''' flag. In that case the total number of cores matlabsubmit will request is ''#workers*#threads + 1''. matlabsubmit will set the number of threads for both the client and all the workers.
 
 
==Matlab Cluster Profiles==
 
 
In addition to the 50 general Matlab licenses, HPRC also purchased a Matlab Distributed Computing Server license for a total 96 tokens. These tokens are used to start additional Matlab workers and are used by parallel Matlab constructs like ''parfor'', ''spmd'', and ''distributed''.
 
 
For parallel processing on the compute nodes Matlab uses Cluster profiles. A cluster profile acts as an interface between Matlab and the batch scheduler (e.g. LSF, SLURM) and lets you define certain properties of your cluster (e.g. how to submit jobs, submission parameters, job requirements, etc). Matlab will use the cluster profile to offload parallel (or sequential) matlab code to one or more workers.
 
  
 +
This function imports the cluster profile and it creates a directory structure in your scratch directory where Matlab will store meta-information during parallel processing. The default location is ''/scratch/$USER/MatlabJobs/TAMU<VERSION'', where <VERSION> represents the Matlab version. For example, for Matlab R2019b it will be ''/scratch/$USER/MatlabJobs/TAMU2019b''
  
For your convenience, HPRC already created a custom Cluster Profile. You can use this profile to define how many workers you want, how you want to distribute the workers over the nodes Before you can use this profile you need to import it first (you only need to do this once). This can be done using by calling the following Matlab function.
+
<!--
 
+
'''NOTE:''' function '''tamuprofile.clusterprofile''' is a wrapper around the Matlab function  
 +
[https://www.mathworks.com/help/distcomp/parallel.importprofile.html parallel.importprofile]
 +
-->
  
 +
'''NOTE:''' For Matlab versions before R2019b, use the following function
 
<pre>
 
<pre>
 
>>tamu_import_TAMU_clusterprofile()
 
>>tamu_import_TAMU_clusterprofile()
 
</pre>
 
</pre>
  
This function imports the cluster profile into the workspace and it also creates a sub directory structure in you scratch to store job information for that cluster
+
In this case, Matlab will store meta-information in directory ''/scratch/$USER/MatlabJobs/TAMU''
  
 +
===  Getting Cluster Profile Object ===
  
We will discuss briefly some of the most common parallel matlab concepts. For more detailed information about these constructs, as well as additional parallel constructs consult the Parallel Computing Toolbox User Guide
+
To return a fully completed cluster object (i.e. with attached resource information) HPRC created the '''tamu_set_profile_properties''' convenience function. There are two steps to follow:
matlabpool
 
  
The matlabpool functions enables the full functionality of the parallel language features (parfor and spmd, will be discussed below). matlabpool creates a special job on a pool of workers, and connects the pool to the MATLAB client. For example:
+
* define the properties using the TAMUClusterProperties class
matlabpool open 4
+
* call '''tamu_set_profile_properties''' using the created TAMUClusterProperties object.
    :
 
    :
 
matlabpool close
 
This code starts a worker pool using the default cluster profile, with 4 additional workers.  
 
  
NOTE: only instructions within parfor and spmd blocks are executed on the workers. All other instructions are executed on the client.  
+
For example, suppose you have Matlab code and want to use 4 workers for parallel processing.  
 
+
NOTE: all variables declared inside the matlabpool block will be destroyed once the block is finished.
+
<pre>
 +
>> tp=TAMUClusterProperties;
 +
>> tp.workers(4);
 +
>> clusterObject=tamu_set_profile_properties(tp);
 +
</pre>
  
For more detailed information please visit the Matlab matlabpool page.
+
Variable ''clusterObject'' is a fully populated cluster object that can be used for parallel processing.  
parfor
 
  
The concept of a parfor-loop is similar to the standard Matlab for-loop. The difference is that parfor partitions the iterations among the available
+
'''NOTE:''' convenience function '''tamu_set_profile_properties''' is a wrapper around Matlab function
workers to run in parallel. For example:
+
[https://www.mathworks.com/help/distcomp/parcluster.html parcluster]. It also uses HPRC convenience function '''tamu_import_TAMU_clusterprofile''' to check if the '''TAMU''' profile has been imported already.
 
 
<pre>
 
matlabpool open  2
 
parfor i=1:1024
 
  A(i)=sin((i/1024)*2*pi);
 
end
 
matlabpool close
 
</pre>
 
This code will open a matlab pool with 2 workers using the default cluster profile and execute the loop in parallel.  
 
  
For more information please visit the Matlab parfor page.
+
== Starting a Parallel Pool ==
spmd
 
  
spmd runs the same program on all workers concurrently. A typical use of spmd is when you need to run the same program on multiple sets of input. For example, Suppose you have 4 inputs named data1,data2,data3,data4 and you want run funcion myfun on all of them:
+
To start a parallel pool you can use the HPRC convenience function '''tamu_parpool'''. It takes as argument a '''TAMUClustrerProperties''' object that specifies all the resources that are requested.  
  
 +
The '''parpool''' functions enables the full functionality of the parallel language features (parfor and spmd, will be discussed below). A parpool creates a special job on a pool of workers, and connects the pool to the MATLAB client. For example:
 
<pre>
 
<pre>
matlabpool open  4
+
mypool = parpool 4
spmd (4)
+
     :
     data = load(['data' num2str(labindex)])
+
delete(mypool)
    myresult = myfun(data)
 
end
 
matlabpool close
 
 
</pre>
 
</pre>
NOTE: labindex is a Matlab variable and is set to the worker id, values range from 1 to number of workers.
 
  
Every worker will have its own version of variable myresult. To access these variables outside the spmd block you append {i} to the variable name, e.g. myresult{3} represents variable myresult from worker 3.  
+
This code starts a worker pool using the default cluster profile, with 4 additional workers.  
  
For more information please visit the Matlab spmd page.
+
NOTE: only instructions within parfor and spmd blocks are executed on the workers. All other instructions are executed on the client.  
batch
 
 
 
The parallel constructs we discussed so far are all interactive, meaning that the client starts the workers and then waits for completion of the job before accepting any other input. The batch command will submit a job and return control back to the client immediately. For example, suppose we want to run the parfor loop from above without waiting for the result. First create a matlab function myloop.m
 
 
 
<pre>
 
parfor i=1:1024
 
  A(i)=sin((i/1024)*2*pi);
 
end
 
</pre>
 
  
To run using the batch command:
+
NOTE: all variables declared inside the matlabpool block will be destroyed once the block is finished.
myjob = batch('myloop','matlabpool',4)
 
This will start the parallel job on the workers and control is returned to the client immediately. To see all your running jobs click on Parallel/Monitor Jobs. Use the wait command, e.g. wait(myjob), to wait for the job to finish, use the load command, e.g. load(myjob), to load all variables from the job into the client workspace.  
 
  
For more information please visit the Matlab batch page.
+
== Using GPU ==
Using GPU
 
  
Normally all variables reside in the client workspace and matlab operations are executed on the client machine (e.g. your desktop, or an eos login node). However, Matlab also provides options to utilize available GPUs to run code faster.
+
Normally all variables reside in the client workspace and matlab operations are executed on the client machine. However, Matlab also provides options to utilize available GPUs to run code faster.
 
Running code on the gpu is actually very straightforward. Matlab provides GPU versions for many build-in operations. These operations are executed on the GPU automatically when the variables involved reside on the GPU. The results of these operations will also reside on the GPU. To see what functions can be run on the GPU type:
 
Running code on the gpu is actually very straightforward. Matlab provides GPU versions for many build-in operations. These operations are executed on the GPU automatically when the variables involved reside on the GPU. The results of these operations will also reside on the GPU. To see what functions can be run on the GPU type:
  
Line 362: Line 187:
 
gpuDevice
 
gpuDevice
 
This functions shows all the properties of the GPU. When this function is called from the client (or a node without a GPU) it will just print an error message.
 
This functions shows all the properties of the GPU. When this function is called from the client (or a node without a GPU) it will just print an error message.
Adjusting Cluster Profile
 
  
to use the gpus on EOS we need to adjust the job requirements to make sure the job is scheduled on a node with a gpu, the same way you would do it with a regular eos job.
 
<pre>
 
dcluster = parcluster
 
dcluster.ResourceTemplate='-l nodes=1:ppn=1:gpus=1,walltime=02:00:00,mem=20gb'
 
</pre>
 
 
The above job requirements are just an example. You can adjust the various properties to suit your needs.
 
More detailed information about changing Profile Properties can be found here
 
Copying between client and GPU
 
  
 
To copy variables from the client workspace to the GPU, you can use the gpuArray command. For example:
 
To copy variables from the client workspace to the GPU, you can use the gpuArray command. For example:
Line 380: Line 195:
 
</pre>
 
</pre>
  
will copy variable carr to the GPU wit name garr. If variable carr is not used in the client workspace you can write it as:
+
will copy variable carr to the GPU wit name garr.  
  
<pre>
+
In the example above the 1000x1000 matrix needs to be copied from the client workspace to the GPU. There is a significant overhead involved in doing this.
garr = gpuArray(ones(1000));
 
</pre>
 
  
The two versions have the same problem. They both need to copy the 1000x1000 matrix from client workspace to the GPU. We mentioned above that Matlab provides methods to create data directly on the GPU to avoid the overhead of copying data to the GPU. For example:
+
To create the variables directly on the GPU, Matlab provides a number of convenience functions. For example:
 
<pre>
 
<pre>
 
garr=gpuArray.ones(1000)
 
garr=gpuArray.ones(1000)
Line 392: Line 205:
  
 
This will create a 1000x1000 matrix directly on the GPU consisting of all ones.  
 
This will create a 1000x1000 matrix directly on the GPU consisting of all ones.  
 
You can find a list of all methods to create data directly on the GPU here.
 
  
  
Line 402: Line 213:
  
 
This will copy the array garr on the GPU back to variable carr2 in the client workspace.
 
This will copy the array garr on the GPU back to variable carr2 in the client workspace.
Overhead
 
  
As mentioned before there is considerable overhead involved when using the GPU. Actually, there are two types of overhead.
+
The next example performs a matrix multiplication on the client, a matrix multiplication on the GPU, and prints out elapsed times for both. The actual cpu-gpu matrix multiplication code can be written as:
Warming up GPU (first time GPU is used).
+
<pre>
Data transfer.
+
ag = gpuArray.rand(1000);
Warming up
+
bg = ag*ag;
 +
c = gather(bg);
 +
</pre>
 +
 
 +
= Running (parallel) Matlab Scripts on HPRC compute nodes =
 +
 
 +
'''NOTE:''' Due to the new 2-factor authentication mechanism, this method does not work at the moment. We will update this wiki page when this is fixed.
  
When the GPU is just starting up computation, there are many things that need to be done, both on the Matlab part and the GPU device itself (e.g. loading libraries, initializing the GPU state, etc). For example:
 
matlabpool open 1
 
spmd 1
 
tic
 
gpuArray.ones(10,1);
 
toc
 
end
 
This code only creates a 10x1 array of ones on the GPU device. The first run takes an astounding 21.5 seconds to execute while every successive run only needs about 0.00017 seconds. This shows the huge cost of warming up the GPU.
 
  
NOTE:These are running times on EOS. Other systems might have very different timing results.
+
For detailed information how to submit Matlab codes remotely, click [[SW:Matlab_app | here]]
Data transfer
 
  
GPU operations in Matlab can only be done when the data is physically located on the GPU device. Therefore data might need to be transferred to the GPU device (and vice versa). This is a significant overhead. For example:
+
== Submit Matlab Scripts Remotely or Locally From the Matlab Command Line  ==
spmd 1
 
tic;ag=gpuArray(ones(10000));toc;
 
end
 
The above code only copies a 10000x10000 matrix from client workspace to GPU device. The time it takes is almost 0.6 seconds. This is a significant overhead.
 
Example
 
  
Here is a little example that performs a matrix multiplication on the client, a matrix multiplication on the GPU, and prints out elapsed times for both. The actual cpu-gpu matrix multiplication code can be written as:
+
'''NOTE:''' Due to the new 2-factor authentication mechanism, remote submission method does not work at the moment. We will update this wiki page when this is fixed.
a =  rand(1000);
 
tic; b = a*a; toc;
 
tic; ag = gpuArray(a); bg = ag*ag; toc;
 
c = gather(cg)
 
Almost no additional steps are required to use the gpu. Actually, copying the results to the client workspace is not even needed. Variables that reside on the gpu can be printed or plotted just like variables in the client workspace.  
 
  
The above code will run without problems if Matlab is installed on a computer with a gpu attached. Since EOS does not have gpus attached to the login nodes (where the client is running) we need to ensure the above code is run on a gpu node. We will show how to do it in interactive mode (using matlabpool), and by using the Matlab batch command.
+
Instead of using the App you can also call Matlab functions (developed by HPRC) directly to run your Matlab script on HPRC compute nodes. There are two steps involved in submitting your Matlab script:
  
For convenience the code above is saved as mymatrixmult.m
+
* Define the properties for your Matlab script (e.g. #workers).  HPRC created a class named '''TAMUClusterProperties''' for this
Interactive using matlabpool
+
* Submit the Matlab script to run on HPRC compute nodes. HPRC created a function named '''tamu_run_batch''' for this.
  
A matlabpool needs to be opened since a gpu node is needed and the client is running on one of the login nodes (no gpu available) and mymatrixmult needs to be inside a spmd block to ensure code will actually run on the worker instead of the client (see matlabpool section). The code will be as follows:
+
For example, suppose you have a script named ''mysimulation.m'', you want to use 4 workers and estimate it will need less than 7 hours of computing time:  
matlabpool open 1
 
spmd 1
 
mymatrixmult
 
end
 
matlabpool close
 
Using Matlab batch command
 
  
This example is a basic sequential code (i.e. uses only one cpu core), so in this case a matlabpool is not even needed. The Matlab batch command will start the job on one of the workers (which has a gpu). The code will look as follows:
+
<pre>
batch('mymatrixmult')
+
>> tp=TAMUClusterProperties();
Warming up the GPU
+
>> tp.workers(4);
 +
>> tp.walltime('07:00');
 +
>> myjob=tamu_run_batch(tp,'mysimulation.m');
 +
</pre>
 +
 
 +
'''NOTE:''' '''TAMUClusterProperties''' will use all default values for any of the properties that have not been set explicitly.  
 +
 
 +
In case you want to submit your Matlab script remotely from your local Matlab GUI, you also have to specify the HPRC cluster name you want to run on and your username.
 +
For example, suppose you have a script that uses Matlab GPU functions and you want to run it on terra:
 +
<pre>
 +
>> tp=TAMUClusterProperties();
 +
>> tp.gpu(1);
 +
>> tp.hostname('terra.tamu.edu');
 +
>> tp.user('<USERNAME>'); 
 +
>> myjob=tamu_run_batch(tp,'mysimulation.m');
 +
</pre>
 +
 
 +
To see all available methods on objects of type '''TAMUClusterProperties''' you can use the Matlab '''help''' or '''doc''' functions: E.g.
 +
 
 +
  >> help TAMUClusterProperties/doc
 +
 
 +
To see help page for '''tamu_run_batch''', use:
 +
 
 +
<pre>
 +
  >> help tamu_run_batch
 +
      tamu_run_batch  runs Matlab script on worker(s).  
 +
 +
        j = TAMU_RUN_BATH(tp,'script') runs the script
 +
        script.m on the worker(s) using the TAMUClusterProperties object tp.
 +
        Returns j, a handle to the job object that runs the script.
 +
 
 +
 
 +
</pre>
  
there is considerable overhead involved when using the GPU. Besides the data transfer overhead mentioned before, there is another kind of overhead; warming up time. When the GPU is just starting up computation, there are many things that need to be done, both on the Matlab part and the GPU device itself (e.g. loading libraries, initializing the GPU state, etc). To get an indication how much time is needed look at the following example:
 
                                                                                   
 
matlabpool open 1
 
spmd 1
 
tic
 
gpuArray.ones(10,1);
 
toc
 
end
 
This code only creates a 10x1 array of ones on the GPU device. The first run takes 0.026 seconds to execute while every sucessive run only needs about 0.00017 seconds (of course different runs will produce slightly different results). This shows the huge cost of warming up the GPU .
 
  
NOTE:These are running times on EOS. Other systems might have very different timing results.
+
'''tamu_run_batch''' returns a variable of type '''Job'''. See the section ''"Retrieve results and information from Submitted Job"'' how to get results and information from the submitted job.
  
  
 
[[Category:Software]]
 
[[Category:Software]]

Revision as of 23:58, 24 February 2020

Running Matlab interactively

Matlab is accessible to all HPRC users within the terms of our license agreement. If you have particular concerns about whether specific usage falls within the TAMU HPRC license, please send an email to HPRC Helpdesk. You can start a Matlab session either directly on a login node or through our portal

Running Matlab on a login node

To be able to use Matlab, the Matlab module needs to be loaded first. This can be done using the following command:

[ netID@cluster ~]$ module load Matlab/R2019a

This will setup the environment for Matlab version R2019a. To see a list of all installed versions, use the following command:

[ netID@cluster ~]$ module spider Matlab

Note: New versions of software become available periodically. Version numbers may change.

To start matlab, use the following command:

[ netID@cluster ~]$ matlab

Depending on your X server settings, this will start either the Matlab GUI or the Matlab command-line interface. To start Matlab in command-line interface mode, use the following command with the appropriate flags:

[ netID@cluster ~]$ matlab -nosplash -nodisplay

By default, Matlab will execute a large number of built-in operators and functions multi-threaded and will use as many threads (i.e. cores) as are available on the node. Since login nodes are shared among all users, HPRC restricts the number of computational threads to 8. This should suffice for most cases. Speedup achieved through multi-threading depends on many factors and in certain cases. To explicitly change the number of computational threads, use the following Matlab command:

>>feature('NumThreads',4);

This will set the number of computational threads to 4.

To completely disable multi-threading, use the -singleCompThread option when starting Matlab:

[ netID@cluster ~]$ matlab -singleCompThread

Usage on the Login Nodes

Please limit interactive processing to short, non-intensive usage. Use non-interactive batch jobs for resource-intensive and/or multiple-core processing. Users are requested to be responsible and courteous to other users when using software on the login nodes.

The most important processing limits here are:

  • ONE HOUR of PROCESSING TIME per login session.
  • EIGHT CORES per login session on the same node or (cumulatively) across all login nodes.

Anyone found violating the processing limits will have their processes killed without warning. Repeated violation of these limits will result in account suspension.
Note: Your login session will disconnect after one hour of inactivity.

Running Matlab through the hprc portal

HPRC provides a portal through which users can start an interactive Matlab GUI session inside a web browser. For more information how to use the portal see our HPRC OnDemand Portal section

Running Matlab through the batch system

HPRC developed a tool named matlabsubmit to run Matlab simulations on the HPRC compute nodes without the need to create your own batch script and without the need to start a Matlab session. matlabsubmit will automatically generate a batch script with the correct requirements. In addition, matlabsubmit will also generate boilerplate Matlab code to set up the environment (e.g. the number of computational threads) and, if needed, will start a parpool using the correct Cluster Profile (local if all workers fit on a single node and a cluster profile when workers are distribued over multiple nodes)

To submit your Matlab script, use the following command:

[ netID@cluster ~]$ matlabsubmit myscript.m

In the above example, matlabsubmit will use all default values for runtime, memory requirements, the number of workers, etc. To specify resources, you can use the command-line options of matlabsubmmit. For example:

[ netID@cluster ~]$ matlabsubmit -t 07:00 -s 4 myscript.m

will set the wall-time to 7 hours and makes sure Matlab will use 4 computational threads for its run ( matlabsubmit will also request 4 cores).

To see all options for matlabsubmit use the -h flag

[ netID@cluster ~]$ matlabsubmit -h
Usage: /sw/hprc/sw/Matlab/bin/matlabsubmit [options] SCRIPTNAME

This tool automates the process of running Matlab codes on the compute nodes.

OPTIONS:
  -h Shows this message
  -m set the amount of requested memory in MEGA bytes(e.g. -m 20000)
  -t sets the walltime; form hh:mm (e.g. -t 03:27)
  -w sets the number of ADDITIONAL workers
  -g indicates script needs GPU  (no value needed)
  -b sets the billing account to use 
  -s set number of threads for multithreading (default: 8 ( 1  when -w > 0)
  -p set number of workers per node
  -f run function call instead of script
  -x add explicit batch scheduler option
  
DEFAULT VALUES:
  memory   : 2000 per core 
  time     : 02:00
  workers  : 0
  gpu      : no gpu 
  threading: on, 8 threads


NOTE when using the -f flag to execute a function instead of a script, the function call must be enclosed with double quotes when it contains parentheses. For example: matlabsubmit -f "myfunc(21)"


When executing, matlabsubmit will do the following:

  • generate boilerplate Matlab code to setup the Matlab environment (e.g. #threads, #workers)
  • generate a batch script with all resources set correctly and the command to run Matlab
  • submit the generated batch script to the batch scheduler and return control back to the user


For detailed examples on using matlabsubmit see the examples section.


Using Matlab Parallel Toolbox on HPRC Resources

THIS SECTION IS UNDER CONSTRUCTION

In this section, we will focus on utilizing the Parallel toolbox on HPRC cluster. For a general intro to the Parallel Toolbox see the parallel toolbox section on the Mathworks website. Here we will discuss how to use Matlab Cluster profiles to distribute workers over multiple nodes.

The central concept in most of the discussion below is the TAMUClusterProperties object which we will discuss in more detail in the next section


Cluster Profiles

Matlab Cluster Profiles provide an interface to define properties of how and where to start Matlab workers. There are two kinds of profiles.

  • local profiles: parallel processing is limited to the same node the Matlab client is running.
  • cluster profiles: parallel processing can span multiple nodes; profile interacts with a batch scheduler (e.g. SLURM on terra).

NOTE: we will not discuss local profiles any further here. Processing using a local profile is exactly the same as processing using cluster profiles.


Importing Cluster Profile

For your convenience, HPRC already created a custom Cluster Profile. Using the profile, you can define how many workers you want, how you want to distribute the workers over the nodes, how many computational threads to use, how long to run, etc. Before you can use this profile you need to import it first. This can be done using by calling the following Matlab function.

>>tamuprofile.importProfile()


This function imports the cluster profile and it creates a directory structure in your scratch directory where Matlab will store meta-information during parallel processing. The default location is /scratch/$USER/MatlabJobs/TAMU<VERSION, where <VERSION> represents the Matlab version. For example, for Matlab R2019b it will be /scratch/$USER/MatlabJobs/TAMU2019b


NOTE: For Matlab versions before R2019b, use the following function

>>tamu_import_TAMU_clusterprofile()

In this case, Matlab will store meta-information in directory /scratch/$USER/MatlabJobs/TAMU

Getting Cluster Profile Object

To return a fully completed cluster object (i.e. with attached resource information) HPRC created the tamu_set_profile_properties convenience function. There are two steps to follow:

  • define the properties using the TAMUClusterProperties class
  • call tamu_set_profile_properties using the created TAMUClusterProperties object.

For example, suppose you have Matlab code and want to use 4 workers for parallel processing.

>> tp=TAMUClusterProperties;
>> tp.workers(4);
>> clusterObject=tamu_set_profile_properties(tp);

Variable clusterObject is a fully populated cluster object that can be used for parallel processing.

NOTE: convenience function tamu_set_profile_properties is a wrapper around Matlab function parcluster. It also uses HPRC convenience function tamu_import_TAMU_clusterprofile to check if the TAMU profile has been imported already.

Starting a Parallel Pool

To start a parallel pool you can use the HPRC convenience function tamu_parpool. It takes as argument a TAMUClustrerProperties object that specifies all the resources that are requested.

The parpool functions enables the full functionality of the parallel language features (parfor and spmd, will be discussed below). A parpool creates a special job on a pool of workers, and connects the pool to the MATLAB client. For example:

mypool = parpool 4
    :
delete(mypool)

This code starts a worker pool using the default cluster profile, with 4 additional workers.

NOTE: only instructions within parfor and spmd blocks are executed on the workers. All other instructions are executed on the client.

NOTE: all variables declared inside the matlabpool block will be destroyed once the block is finished.

Using GPU

Normally all variables reside in the client workspace and matlab operations are executed on the client machine. However, Matlab also provides options to utilize available GPUs to run code faster. Running code on the gpu is actually very straightforward. Matlab provides GPU versions for many build-in operations. These operations are executed on the GPU automatically when the variables involved reside on the GPU. The results of these operations will also reside on the GPU. To see what functions can be run on the GPU type:

methods('gpuArray') This will show a list of all available functions that can be run on the GPU, as well as a list of available static functions to create data on the GPU directly (will be discussed later).

NOTE: There is significant overhead of executing code on the gpu because of memory transfers.

Another useful function is: gpuDevice This functions shows all the properties of the GPU. When this function is called from the client (or a node without a GPU) it will just print an error message.


To copy variables from the client workspace to the GPU, you can use the gpuArray command. For example:

carr = ones(1000);
garr = gpuArray(carr);

will copy variable carr to the GPU wit name garr.

In the example above the 1000x1000 matrix needs to be copied from the client workspace to the GPU. There is a significant overhead involved in doing this.

To create the variables directly on the GPU, Matlab provides a number of convenience functions. For example:

garr=gpuArray.ones(1000)

This will create a 1000x1000 matrix directly on the GPU consisting of all ones.


To copy data back to the client workspace Matlab provides the gather operation.

carr2 = gather(garr)

This will copy the array garr on the GPU back to variable carr2 in the client workspace.

The next example performs a matrix multiplication on the client, a matrix multiplication on the GPU, and prints out elapsed times for both. The actual cpu-gpu matrix multiplication code can be written as:

ag = gpuArray.rand(1000); 
bg = ag*ag;
c = gather(bg); 

Running (parallel) Matlab Scripts on HPRC compute nodes

NOTE: Due to the new 2-factor authentication mechanism, this method does not work at the moment. We will update this wiki page when this is fixed.


For detailed information how to submit Matlab codes remotely, click here

Submit Matlab Scripts Remotely or Locally From the Matlab Command Line

NOTE: Due to the new 2-factor authentication mechanism, remote submission method does not work at the moment. We will update this wiki page when this is fixed.

Instead of using the App you can also call Matlab functions (developed by HPRC) directly to run your Matlab script on HPRC compute nodes. There are two steps involved in submitting your Matlab script:

  • Define the properties for your Matlab script (e.g. #workers). HPRC created a class named TAMUClusterProperties for this
  • Submit the Matlab script to run on HPRC compute nodes. HPRC created a function named tamu_run_batch for this.

For example, suppose you have a script named mysimulation.m, you want to use 4 workers and estimate it will need less than 7 hours of computing time:

>> tp=TAMUClusterProperties();
>> tp.workers(4);
>> tp.walltime('07:00');
>> myjob=tamu_run_batch(tp,'mysimulation.m');

NOTE: TAMUClusterProperties will use all default values for any of the properties that have not been set explicitly.

In case you want to submit your Matlab script remotely from your local Matlab GUI, you also have to specify the HPRC cluster name you want to run on and your username. For example, suppose you have a script that uses Matlab GPU functions and you want to run it on terra:

>> tp=TAMUClusterProperties();
>> tp.gpu(1);
>> tp.hostname('terra.tamu.edu');
>> tp.user('<USERNAME>');  
>> myjob=tamu_run_batch(tp,'mysimulation.m');

To see all available methods on objects of type TAMUClusterProperties you can use the Matlab help or doc functions: E.g.

  >> help TAMUClusterProperties/doc 

To see help page for tamu_run_batch, use:

   >> help tamu_run_batch
      tamu_run_batch  runs Matlab script on worker(s). 
 
         j = TAMU_RUN_BATH(tp,'script') runs the script
         script.m on the worker(s) using the TAMUClusterProperties object tp.
         Returns j, a handle to the job object that runs the script.



tamu_run_batch returns a variable of type Job. See the section "Retrieve results and information from Submitted Job" how to get results and information from the submitted job.